yolov5模型转换中img-size 224 must be multiple of max stride 64, updating to 256如何解决

export: weights=./weights/attention/best001.pt, imgsz=[672, 672], batch_size=1, device=cpu, half=False, inplace=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=11, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['onnx']
YOLOv5 2023-6-8 Python-3.10.11 torch-2.0.1+cpu CPU

Fusing layers...
YOLOv5s6 summary: 280 layers, 12312052 parameters, 0 gradients
WARNING --img-size 672 must be multiple of max stride 64, updating to 704
WARNING --img-size 672 must be multiple of max stride 64, updating to 704
Traceback (most recent call last):
File "D:\yolov5-master\yolov5-master\export.py", line 818, in
main(opt)
File "D:\yolov5-master\yolov5-master\export.py", line 813, in main
run(**vars(opt))
File "D:\yolov5-master\yolov5-master\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "D:\yolov5-master\yolov5-master\export.py", line 712, in run
y = model(im) # dry runs
File "D:\yolov5-master\yolov5-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "D:\yolov5-master\yolov5-master\models\yolo.py", line 209, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "D:\yolov5-master\yolov5-master\models\yolo.py", line 121, in _forward_once
x = m(x) # run
File "D:\yolov5-master\yolov5-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "D:\yolov5-master\yolov5-master\models\yolo.py", line 73, in forward
xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
File "D:\yolov5-master\yolov5-master\venv\lib\site-packages\torch\_tensor.py", line 803, in split
return torch._VF.split_with_sizes(self, split_size, dim)
IndexError: Dimension out of range (expected to be in range of [-4, 3], but got 4)

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